Pests such as weeds, insects, and pathogens occurring in fields, crops and soils are constant problems for the agricultural industry. The presence of a pest in a field is a result of weather interacting with crop and soil management activity. Yet there are few weather-driven pest or disease models available for management decisions in relation to major crops. Also, field-specific risk assessments are only available for very few pests because the required models of pest biology are expensive to develop, despite the high value in crop output. Other issues include a lack of data for proof of concept to validate existing models or to design new models, and inherent apprehension among growers and landowners in admitting pest infestation in their fields and crops.
Some models have been developed to predict pest infestation based on weather variables. For example, development of insect or an insect population has been modeled with growing degree days to predict when the insect or a substantial portion of the insect's population will be present in a field. Disease has been predicted by comparing current or recent climatic factors such as temperature and leaf wetness over a few hours or days against measured climatic factors that are known to be favorable for a particular pathogen. These models, however, do not take into account forecasts of weather data and predictions, and also ignore other factors that interact with weather and lead to infestation. For example, crop management in particular may alter the impact of weather. Also, the crop is often susceptible at certain growth stages and crop development, like insect development, is influenced by variances in weather conditions.
Existing approaches are limited at least in part because attempting to quantify and model all these interactions is overwhelming. Even attempting to identify which are the most important factors that should be modeled is a major challenge due to the constantly-changing parameters during a growing season. Moreover, changing crop management, like different tillage methods or planting of seed varieties across different seasons, requires new studies and constant updating of models.
One existing approach to pest management is simply to compare climatic factors in un-infested and infested fields. Climatic factors in such an approach are explicitly handled as time series of data, and compared using methods to assess the similarity of time series. For example, a similarity of the pattern of average temperature over a series of days in infested versus un-infested fields would be calculated. Growing degrees days is one measure of temperature over a period, but growing degree days but does not explicitly consider such a pattern. The same value of growing degree days may be accumulated from several different patterns of weather over a set of days.
Other existing approaches to providing information regarding an agricultural pest infestation include websites that map observations of pest scouting or counts of pests from traps, along with weekly emails describing pest problems reported to extension specialists. However, constraints of cost and data privacy mean universities cannot provide information about crop management associated with specific pest-infested fields. Managers must therefore guess at the relevance of the information to their own fields, greatly reducing the reliability of such information.
Also, weather and crop information is also often imprecise on such websites, and therefore mapping only relates pest occurrence in a general way to weather. The resulting resolution of the information is poor, and pest presence is not related to specific weather variables or specific crop management activities. The manager must therefore speculate how crop management and growth stage factors in the mapped infested areas mitigate or promote pest presence, and for the number of fields in which the pest was observed. Managers must also speculate as to what weather conditions promoted the presence of the pest, and whether there are similar conditions forecast or prevailing in his or her managed fields. Managers will therefore not be able to accurately determine what crop management activities mitigate or promote pest infestation, nor will they be able to determine which of fields have a high priority for scouting or treatment.
Many techniques are available for obtaining crowd-sourced information. Often, crowd-sourced information is crucial to containing a spread of a pest or disease over a wider area because of the real-time nature of such ground truth observations. However, the effectiveness of crowd-sourcing for pest management is sharply limited by the reluctance of growers to reveal the presence of pests in their fields to others. This reluctance is harmful, as speedy knowledge of an infestation in a nearby field can help the wider region contain the spread and avoid costly damage with quick action. There is no existing approach to pest management that leverages crowd-sourced reporting anonymously, so that growers can feel comfortable with accurate reporting of pest and disease issues in their own crops and fields.